Wrapper function for different clustering methods
Makes predictions from the model with the smallest cross-validation error.
matrix multiply
Fits an MCEN model
predictions from a mcen model
Creates the probabilities and working response for the glmnet update for a given response with a binomial family
Calculates cluster assignment and coefficient estimates for a binomial mcen.
Provides initial estimates for the mcen functionF
Cross validation for mcen function
Gets the index position for the model with the smallest cross-validation error.
Evaluates prediction error for multiple binomial responses.
Prints nice output for a cv.mcen object.
Prints nice output for an mcen object.
Calculates the prediction error for a mgauss_mcen object.
Calculates out of sample error on the binomial likelihood
Estimates the clusters and provides the coefficients for an mcen object
Returns the cluster values from a cv.mcen object.
Calculates the out of sample likelihood for an mcen object
randomly assign n samples to k groups
Calculates sum of squared error between two vectors or matrices
SetEq test set equivalence of two clustering sets
Adjusts the value of the binomial coefficients to account for the scaling of x.
Returns the coefficients from the cv.mcen object with the smallest cross-validation error.
The workhorse function for the binomial updates in mcen. It uses IRWLS glmnet updates to solve the regression problem.
Creates the the working response for all responses for glmnet binomial family
Adjusts the value of the coefficients to account for the scaling of x and y.
Returns the coefficients from an mcen object.